In petrochemicals and refinery processes the rapid and efficient detection of deviations from normal or ideal operation is important in maintaining production efficiency. In a typical process, alarm points are typically set for important operating parameters, such as temperatures, pressures and flow rates in various parts of the process, which alarm points can suitably be displayed on a control unit monitor. The alarm points may be used to alert an operator through a visual and/or audible signal so that manual intervention, if necessary, can be taken.
Often, methods of fault detection are univariate in nature, in which each process variable is analysed and controlled independently. However, Martin et al, in EE Proc.-Control Theory Appl., 143(2), pp 132-144 (1996), describe the use of multivariate statistical methods in detecting abnormal events in a process. In one example, predicted values of dependent variables of a process can be obtained by performing partial least squares (PLS) analysis based on the values of independent variables. Differences between the predicted values from the PLS analysis and the measured dependent variable values are then used to highlight any deviations from expected or optimal behaviour.
It has also been disclosed by Wachs and Lewin in Dynamics and Control of Process Systems, 1998 Vol. 1, pp 87-92 (Oxford) that performing principal component analysis on the residual values between a first principles model and a simulated process, i.e. the differences between the predicted and calculated values of the dependent variables, can provide an accurate means of identifying any deviations from expected or optimal behaviour, and also which variables are involved in the deviation.
A problem with such methods, however, is that both a historical database of process data and a model developed from first principles are required for predictive analysis, which are time consuming and costly to develop and implement.